Nick Cammarata / Goodfire : A researcher says modern neural networks are far more interpretable than believed, with most neurons understandable in models up to trillions of parameters — The cause of polysemanticity in early model organisms wasn't superposition, but several ordinary, mostly fixable issues.
A researcher says modern neural networks are far more interpretable than believed, with most neurons understandable in models up to trillions of parameters (Nick Cammarata/Goodfire)
In short
Specifically, Cammarata states that most neurons in models up to trillions of parameters can be understood by humans. This finding suggests that the polysemanticity in early model organisms, which was previously attributed to superposition, is actually due to several ordinary, fixable issues.
Key points
- Specifically, Cammarata states that most neurons in models up to trillions of parameters…: Specifically, Cammarata states that most neurons in models up to trillions of parameters can be understood by humans.
- This finding suggests that the polysemanticity in early model organisms, which was previo…: This finding suggests that the polysemanticity in early model organisms, which was previously attributed to superposition, is actually due to several ordinary, fixable issues.